MRI-assessed tumor-free distance to serosa predicts deep myometrial invasion and poor outcome in endometrial cancer

Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC). Methods Preoperative pelvic MRI of 357 included pa...

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Bibliographic Details
Published in:Insights into imaging 2022-01, Vol.13 (1), p.1-1, Article 1
Main Authors: Dybvik, Julie Andrea, Fasmer, Kristine E., Ytre-Hauge, Sigmund, Husby, Jenny Hild Aase, Salvesen, Øyvind O., Stefansson, Ingunn Marie, Krakstad, Camilla, Trovik, Jone, Haldorsen, Ingfrid S.
Format: Article
Language:English
Subjects:
Biomarkers
Cancer
Diagnostic Radiology
Diagnostic systems
Diameters
Endometrial cancer
Endometrial neoplasm
Imaging
Internal Medicine
Interventional Radiology
Magnetic resonance imaging
Medical imaging
Medicine
Medicine & Public Health
Multivariate analysis
Neuroradiology
Original
Original Article
Predictions
Progression-free survival
Radiologists
Radiology
Statistical models
Subjective assessment
Tumors
Ultrasound
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Summary:Objectives To explore the diagnostic accuracy of preoperative magnetic resonance imaging (MRI)-derived tumor measurements for the prediction of histopathological deep (≥ 50%) myometrial invasion (pDMI) and prognostication in endometrial cancer (EC). Methods Preoperative pelvic MRI of 357 included patients with histologically confirmed EC were read independently by three radiologists blinded to clinical information. The radiologists recorded imaging findings (T1 post-contrast sequence) suggesting deep (≥ 50%) myometrial invasion (iDMI) and measured anteroposterior tumor diameter (APD), depth of myometrial tumor invasion (DOI) and tumor-free distance to serosa (iTFD). Receiver operating characteristic (ROC) curves for the prediction of pDMI were plotted for the different MRI measurements. The predictive and prognostic value of the MRI measurements was analyzed using logistic regression and Cox proportional hazard model. Results iTFD yielded highest area under the ROC curve (AUC) for the prediction of pDMI with an AUC of 0.82, whereas DOI, APD and iDMI yielded AUCs of 0.74, 0.81 and 0.74, respectively. Multivariate analysis for predicting pDMI yielded highest predictive value of iTFD <  6 mm with OR of 5.8 ( p  
ISSN:1869-4101
1869-4101
DOI:10.1186/s13244-021-01133-z